Coordinated optimization scheduling method for electric vehicles and distribution network considering new energy consumption rate

Abstract

This invention relates to a method for coordinated optimization scheduling of electric vehicles and distribution networks that takes into account the renewable energy absorption rate, comprising the following steps: inputting relevant parameters of the distribution network, the highest and lowest charging prices, giving an initial value for the charging price, and setting... k =1; The initial charging price is transmitted to each charging station, and each charging station collects electric vehicle charging information; each charging station performs optimized scheduling and formulates the optimal charging plan; the total charging power of each charging station is returned to the distribution network, and the distribution network performs optimized scheduling and calculates the optimal power flow; based on the optimal power flow, the peak-valley difference of the total load of the distribution network, load fluctuation, and the total power curtailment of distributed generation are calculated; a fitness function of the particle swarm optimization algorithm is constructed to guide the solution of the charging price; it is determined whether the charging price has reached the convergence condition. If so, the result is output, the calculation ends, the charging price is transmitted to each charging station, and the optimized scheduling of the charging stations is returned for recalculation. This invention effectively smooths the peak-valley difference of the load and improves the renewable energy absorption rate.

Description

Technical Field

[0001] This invention relates to the field of power system distribution network and charging station collaborative optimization operation technology, and in particular to a method for collaborative optimization scheduling of electric vehicles and distribution networks that takes into account the renewable energy absorption rate. Background Technology

[0002] With the rapid development of the new energy vehicle industry and the in-depth promotion of the "dual carbon" strategy, the large-scale grid connection of electric vehicles in the distribution network has become the norm. The large-scale and random charging and discharging behavior of electric vehicles brings new challenges to the safe, stable and economical operation of the distribution network. On the one hand, users' daily travel habits lead to the electric vehicle charging load having strong randomness, concentration and temporal fluctuation. A large number of vehicles are concentrated in the evening peak charging, which will significantly increase the peak-valley difference of the distribution network load, increase network losses, and easily cause problems such as line overload and low node voltage, thus deteriorating the power supply quality of the distribution network. On the other hand, the penetration rate of intermittent distributed new energy sources such as wind power and photovoltaics in the distribution network continues to increase. The intermittency and uncertainty of new energy output further aggravates the power fluctuation and voltage over-limit risk of the distribution network. The load regulation and voltage control capabilities of the traditional distribution network are difficult to adapt to the dual operating pressure of new energy consumption and large-scale electric vehicle access. Unlike traditional fixed reactive power compensation equipment, relying on V2G vehicle-to-grid interaction technology, electric vehicles can serve as a large-scale, mobile, distributed, and adjustable energy storage resource. They have multiple functions such as load absorption, reverse power transmission, and power regulation, and can flexibly participate in distribution network load peak shaving and valley filling, voltage optimization and control, and new energy consumption auxiliary services. They can effectively smooth distribution network power fluctuations, reduce network losses, and alleviate node voltage deviation problems. They are an important adjustable resource for improving the flexible control capability of active distribution networks and realizing the coordinated and optimized operation of source, grid, load, and storage. Therefore, guiding electric vehicles to participate in distribution network optimization and scheduling in an orderly manner has important engineering value and practical significance for improving the economic efficiency of distribution network operation, power supply stability, and new energy consumption capacity. Existing technical solutions for electric vehicles participating in distribution network optimization scheduling still have significant shortcomings and deficiencies. In terms of optimization scheduling methods, existing scheduling strategies mostly focus on the scheduling costs of electric vehicle charging and discharging, and the operating costs of the distribution network, in order to improve the economic efficiency of electric vehicle charging and the economic efficiency of distribution network operation. However, existing strategies have not fully explored the potential of electric vehicles to promote the consumption of renewable energy through coordinated operation with distributed power sources, energy storage, reactive power compensation equipment and other diverse adjustable resources in the distribution network. As a result, the electric vehicle charging load has not been able to fully play its role in promoting the consumption of renewable energy, resulting in a waste of the regulation potential of electric vehicles.

[0003] Therefore, this invention proposes a collaborative optimization scheduling method for electric vehicles and distribution networks that takes into account the renewable energy absorption rate. In the optimization scheduling process, the economic efficiency of the collaborative operation of electric vehicles and various distributed adjustable resources of the distribution network is comprehensively considered. At the same time, the power curtailment of distributed power sources, the load fluctuation of the distribution network, and the load peak-valley difference are taken as core control targets to achieve multi-resource collaborative scheduling of the distribution network and charging stations. While ensuring the economic efficiency of operation, the method aims to smooth the load peak-valley difference and improve the absorption rate. Summary of the Invention

[0004] (a) Technical problems to be solved To address the problem that existing control methods cannot fully leverage the role of electric vehicles in promoting the absorption of renewable energy, this invention provides a method for coordinated and optimized scheduling of electric vehicles and power distribution networks that takes into account the renewable energy absorption rate, thereby improving the renewable energy absorption rate while ensuring economic efficiency.

[0005] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: a method for coordinated optimization scheduling of electric vehicles and power distribution networks, taking into account the renewable energy absorption rate, characterized by comprising the following steps: Step 1: Input the distribution network parameters, basic daily load power, maximum charging price, and minimum charging price. Provide the initial charging price values ​​for each hour of the day, and set the number of iterations. k =1, which is denoted as the first iteration of the particle swarm; Step 2: Transmit the initial charging price value at each hour to each charging station, and each charging station collects the charging information of electric vehicles within the station; Step 3: Based on electric vehicle charging information and charging prices at various times, optimize the scheduling of electric vehicles in each charging station, formulate the optimal charging plan for each electric vehicle, and calculate the total charging power of each charging station at each time. Step 4: Return the total charging power of each charging station at each time to the distribution network. The distribution network will optimize the scheduling of adjustable equipment based on the returned total charging power, distribution network parameters, and basic daily load power, and calculate the optimal power flow of the distribution network after superimposing the charging power of the charging stations. Step 5: Based on the calculated optimal power flow results of the distribution network, calculate the peak-to-valley difference of the total load, load fluctuation, and total power curtailment of distributed generation. Step 6: Incorporate the peak-to-valley difference of the total load of the distribution network, load fluctuation, and the total power curtailment of distributed generation into the fitness function of the particle swarm optimization algorithm to guide the solution of the charging electricity price at each hour of the 24-hour period that meets the constraints. Step 7: Determine if the charging price has reached the convergence condition. If yes, proceed to Step 8; otherwise, let... k = k+1, transmit the obtained charging price to each charging station, and proceed to step three; Step 8: Output the electric vehicle charging price, the total charging power of each charging station at each time, the total load power of the distribution network at each time, and the renewable energy consumption rate.

[0006] As a preferred embodiment of the electric vehicle and distribution network collaborative optimization scheduling method that takes into account the renewable energy absorption rate described in this invention, in step one: the basic daily load power of the distribution network is input according to the hourly power input, with 24 data points per day, and the initial value of the charging price is randomly generated within the range of the highest and lowest charging prices.

[0007] As a preferred embodiment of the electric vehicle and power grid collaborative optimization scheduling method that takes into account the renewable energy absorption rate described in this invention, the electric vehicle charging information in step two includes grid connection time, departure time, initial state of charge, expected state of charge at departure, rated charging power, and battery capacity.

[0008] As a preferred embodiment of the electric vehicle and distribution network collaborative optimization scheduling method considering the renewable energy absorption rate described in this invention, in step three, the electric vehicles are optimized and scheduled to meet the continuous charging needs of each electric vehicle within its parking time and minimize charging costs. When an electric vehicle is charging, the charging power is the rated charging power; when not charging, the charging power is 0. The charging power of each electric vehicle at each time point is determined. The total charging power of each charging station at each time point is: ; In the formula, To connect to the node j charging station t Total charging power at any given moment. To connect to the node j The first charging station i electric vehicles t Charging power at any time m This represents the total number of electric vehicles in the charging station.

[0009] As a preferred embodiment of the electric vehicle and distribution network collaborative optimization scheduling method considering the renewable energy absorption rate described in this invention, wherein: in step four, the distribution network performs optimized scheduling of adjustable equipment, calculates the optimal power flow of the distribution network after superimposing the charging power of charging stations, and the objective function for optimization is: ; In the formula: C operation The total daily operating cost of the power distribution network; C loss and C buyThese are the distribution network loss costs and the distribution network's cost of purchasing electricity from the main grid, respectively. C DG This refers to the operating cost of distributed power sources; the specific costs are as follows: ; ; ; In the formula: c loss Cost per unit of network loss; for t Total network loss power at any given time; T The scheduling period is set to 24 hours. for t The price of electricity purchased from the main grid at all times; for t The total active power that is constantly purchased from the main grid; c DG Unit operating cost of distributed power sources; Ω DG The set of nodes connected to distributed generation in a power distribution network; For access nodes i Distributed power sources in t The active power emitted at all times; The constraints for optimization are: 1) Node power balance constraints ; ; In the formula: P jk,t and Q jk,t They are respectively t The line flows through the time jk The active and reactive power; P ij,t and Q ij,t They are respectively t The line flows through the time ij The active and reactive power; l ij，t for t Time flows by ij The square of the branch current; r ij and x ij They represent the lines respectively. ij Resistance and reactance; For access nodes j Distributed power sources in t Efforts made at all times; Indicates that it is connected to the nodej charging station t Total charging power at any given moment; and They are respectively t Time Node j The active and reactive power of the base load; Indicates that it is connected to the node j Static var compensator in t Efforts made at all times; 2) Power flow constraints of the line ; ; ; ; In the formula: u i，t for t Time Node i The square of the voltage; and Branch roads ij The lower and upper limits of the active power that can be allowed to flow; and Branch roads ij The lower and upper limits of the reactive power that can flow; branch road ij The maximum current that is allowed to flow; 3) Node voltage constraints ; ; In the formula: u j，t for t Time Node j The square of the voltage; U i,max and U i,min Representing nodes respectively i The upper and lower limits of voltage; 4) Output constraints of distributed power sources ; In the formula: For access nodes i Distributed power sources in t Actual output at any given moment; and Access nodes i Distributed power sources in t The lower and upper limits of output at any given moment; 5) Static Var Compensator Operation Constraints ; In the formula: Indicates that it is connected to the node i Static var compensator in t Efforts made at all times; For access nodes i The capacity of the static var compensator; 6) Power backfeed constraint in distribution network ; In the formula, for t The total active power that the distribution network purchases from the main grid at any given time.

[0010] As a preferred embodiment of the electric vehicle and distribution network coordinated optimization scheduling method considering the renewable energy absorption rate described in this invention, wherein the formula for calculating the total load of the distribution network in step five is: ; In the formula, For the distribution network in t Total load power at any given time For the distribution network in t Base load power at any given time To connect to the node j charging station t Total charging power at any given time, Ω EV The set of nodes connected to the electric vehicle charging station; The formula for calculating the peak-valley difference of the total load in a distribution network is: ; In the formula, This represents the maximum value of the total load power of the distribution network. This represents the minimum total load power of the distribution network. The formula for calculating the load fluctuation of the distribution network is as follows: ; In the formula, and For the distribution network at t +1 time and t Total load power at any given time; The formula for calculating the total power curtailment of distributed generation is: ; In the formula, This represents the total power abandoned by distributed power sources. For access nodes i Distributed power sources in t The maximum output at any given moment For access nodes i Distributed power sources in t Actual output at any moment, Ω DG This refers to the set of nodes connected to the distributed power source.

[0011] As a preferred embodiment of the electric vehicle and distribution network collaborative optimization scheduling method considering the renewable energy absorption rate described in this invention, wherein: the fitness function of the particle swarm optimization algorithm in step six is: ; The constraints for solving the problem using the particle swarm optimization algorithm include: ; In the formula, C max and C min These are the highest and lowest charging prices, respectively. k For the number of particle swarm iterations, n The particle number represents the total number of particles in the population, which is 30. Indicates the first k During the nth iteration n Individual particles t Real-time electricity price information and The first k During the nth iteration n Individual particles t +1 time and t The speed of time, The inertia weighting coefficient is set to a constant of 0.729. , The individual learning factor and the global learning factor of the particle swarm are taken as constants of 1.49; For the first n The particles from initialization to the... k The electricity price corresponding to the optimal fitness of the generation. For the first k The electricity price with the best fitness across the entire particle history in the next iteration. , The random numbers are uniformly distributed in the range [0,1]. The particle position stores the updated electricity price information for each iteration. The rate at which particles are updated.

[0012] As a preferred embodiment of the electric vehicle and distribution network collaborative optimization scheduling method considering the renewable energy absorption rate described in this invention, the convergence criterion in step seven is: the sum of the absolute values ​​of the electricity price differences at each time point given in the two calculations is less than 10. -6 .

[0013] (III) Beneficial Effects This invention provides a method for coordinated and optimized scheduling of electric vehicles and power distribution networks that takes into account the renewable energy absorption rate, and has the following beneficial effects: 1. This invention takes into account both the load of the distribution network and the demand for renewable energy consumption during the process of setting charging electricity prices, so that the charging electricity prices can accurately reflect the load fluctuations and renewable energy consumption, thereby accurately guiding the charging behavior of electric vehicles.

[0014] 2. This invention constructs a two-layer optimization model of distribution network and charging station, which fully considers the coordinated operation of electric vehicles and adjustable resources such as distributed power sources in the distribution network. Through the linkage of multiple devices, it achieves the goals of smoothing distribution network load fluctuations, reducing the peak-valley difference of distribution network load, and promoting the consumption of new energy, thereby ensuring the economic efficiency of distribution network operation while improving the new energy consumption rate. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 An improved IEEE 33-node power distribution network diagram for an embodiment; Figure 3 The curves show the base load of the distribution network and the total maximum output of photovoltaic power. Figure 4 To optimize the calculated charging price curve; Figure 5 The load curves of the distribution network before and after superimposing electric vehicle charging power under Strategy 1; Figure 6 The load curves of the distribution network before and after superimposing electric vehicle charging power under Strategy 2; Figure 7 The load curves of the distribution network before and after superimposing electric vehicle charging power under Strategy 3; Figure 8 The load curves of the distribution network before and after superimposing electric vehicle charging power under Strategy 4 are shown. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1 like Figure 1 As shown, the method for coordinated optimization scheduling of electric vehicles and power distribution networks, taking into account the renewable energy absorption rate, includes the following steps: Step 1: Input the distribution network parameters, basic daily load power, maximum charging price, and minimum charging price. Provide the initial charging price values ​​for each hour of the day, and set the number of iterations. k =1, which is denoted as the first iteration of the particle swarm; Step 2: Transmit the initial charging price value at each hour to each charging station, and each charging station collects the charging information of electric vehicles within the station; Step 3: Based on electric vehicle charging information and charging prices at various times, optimize the scheduling of electric vehicles in each charging station, formulate the optimal charging plan for each electric vehicle, and calculate the total charging power of each charging station at each time. Step 4: Return the total charging power of each charging station at each time to the distribution network. The distribution network will optimize the scheduling of adjustable equipment based on the returned total charging power, distribution network parameters, and basic daily load power, and calculate the optimal power flow of the distribution network after superimposing the charging power of the charging stations. Step 5: Based on the calculated optimal power flow results of the distribution network, calculate the peak-to-valley difference of the total load, load fluctuation, and total power curtailment of distributed generation. Step 6: Incorporate the peak-to-valley difference of the total load of the distribution network, load fluctuation, and the total power curtailment of distributed generation into the fitness function of the particle swarm optimization algorithm to guide the solution of the charging electricity price at each hour of the 24-hour period that meets the constraints. Step 7: Determine if the charging price has reached the convergence condition. If yes, proceed to Step 8; otherwise, let... k = k +1, transmit the obtained charging price to each charging station, and proceed to step three; Step 8: Output the electric vehicle charging price, the total charging power of each charging station at each time, the total load power of the distribution network at each time, and the renewable energy consumption rate.

[0018] In step one of this embodiment, the basic daily load power of the distribution network is input at the hour, with 24 data points per day, and the initial value of the charging price is randomly generated within the range of the highest and lowest charging prices.

[0019] In step two of this embodiment, the electric vehicle charging information includes the grid connection time, disconnection time, initial state of charge, expected state of charge upon disconnection, rated charging power, and battery capacity.

[0020] In step three of this embodiment, electric vehicles are optimized and scheduled to meet the continuous charging needs of each electric vehicle within its parking time and minimize charging costs. When an electric vehicle is charging, the charging power is the rated charging power; when not charging, the charging power is 0. The charging power of each electric vehicle at each time point is determined. The total charging power of each charging station at each time point is: ; In the formula, To connect to the node j charging station t Total charging power at any given moment. To connect to the node j The first charging station i electric vehicles t Charging power at any time m This represents the total number of electric vehicles in the charging station.

[0021] In step four of this embodiment, the distribution network performs optimized scheduling of adjustable equipment and calculates the optimal power flow of the distribution network after superimposing the charging power of the charging station. The objective function for optimization is: ; In the formula: C operation The total daily operating cost of the power distribution network; C loss and C buy These are the distribution network loss costs and the distribution network's cost of purchasing electricity from the main grid, respectively. C DG This refers to the operating cost of distributed power sources; the specific costs are as follows: ; ; ; In the formula: c loss Cost per unit of network loss; for t Total network loss power at any given time; T The scheduling period is set to 24 hours. for t The price of electricity purchased from the main grid at all times; for t The total active power that is constantly purchased from the main grid; c DG Unit operating cost of distributed power sources; Ω DG The set of nodes connected to distributed generation in a power distribution network; For access nodes i Distributed power sources in t The active power emitted at all times; The constraints for optimization are: 1) Node power balance constraints ; ; In the formula: P jk,tand Q jk,t They are respectively t The line flows through the time jk The active and reactive power; P ij,t and Q ij,t They are respectively t The line flows through the time ij The active and reactive power; l ij，t for t Time flows by ij The square of the branch current; r ij and x ij They represent the lines respectively. ij Resistance and reactance; For access nodes j Distributed power sources in t Efforts made at all times; Indicates that it is connected to the node j charging station t Total charging power at any given moment; and They are respectively t Time Node j The active and reactive power of the base load; Indicates that it is connected to the node j Static var compensator in t Efforts made at all times; 2) Power flow constraints of the line ; ; ; ; In the formula: u i，t for t Time Node i The square of the voltage; and Branch roads ij The lower and upper limits of the active power that can be allowed to flow; and Branch roads ij The lower and upper limits of the reactive power that can flow; branch road ij The maximum current that is allowed to flow; 3) Node voltage constraints ; ; In the formula: u j，t for t Time Node j The square of the voltage; U i,max and U i,min Representing nodes respectively i The upper and lower limits of voltage; 4) Output constraints of distributed power sources ; In the formula: For access nodes i Distributed power sources in t Actual output at any given moment; and Access nodes i Distributed power sources in t The lower and upper limits of output at any given moment; 5) Static Var Compensator Operation Constraints

[0022] In the formula: Indicates that it is connected to the node i Static var compensator in t Efforts made at all times; For access nodes i The capacity of the static var compensator; 6) Power backfeed constraint in distribution network ; In the formula, for t The total active power that the distribution network purchases from the main grid at any given time.

[0023] In step five of this embodiment, the formula for calculating the total load of the distribution network is: ; In the formula, For the distribution network in t Total load power at any given time For the distribution network in t Base load power at any given time To connect to the node j charging station t Total charging power at any given time, Ω EV The set of nodes connected to the electric vehicle charging station; The formula for calculating the peak-valley difference of the total load in a distribution network is: ; In the formula, This represents the maximum value of the total load power of the distribution network. This represents the minimum total load power of the distribution network. The formula for calculating the load fluctuation of the distribution network is as follows: ; In the formula, and For the distribution network at t +1 time and t Total load power at any given time; The formula for calculating the total power curtailment of distributed generation is: ; In the formula, This represents the total power abandoned by distributed power sources. For access nodes i Distributed power sources in t The maximum output at any given moment For access nodes i Distributed power sources in t Actual output at any moment, Ω DG This refers to the set of nodes connected to the distributed power source.

[0024] In step six of this embodiment, the fitness function of the particle swarm optimization algorithm is: ; The constraints for solving the problem using the particle swarm optimization algorithm include: ; In the formula, C max and C min These are the highest and lowest charging prices, respectively. k For the number of particle swarm iterations, n The particle number represents the total number of particles in the population, which is 30. Indicates the first k During the nth iteration n Individual particles t Real-time electricity price information and The first k During the nth iteration n Individual particles t +1 time and t The speed of time, The inertia weighting coefficient is set to a constant of 0.729. , The individual learning factor and the global learning factor of the particle swarm are taken as constants of 1.49; w andc 1. c The values ​​for 2 are all based on the parameters recommended in existing literature; For the first n The particles from initialization to the... k The electricity price corresponding to the optimal fitness of the generation. For the first k The electricity price with the best fitness across the entire particle history in the next iteration. , The random numbers are uniformly distributed in the range [0,1]. The particle position stores the updated electricity price information for each iteration. The rate at which particles are updated.

[0025] In step seven of this embodiment, the convergence criterion is: the sum of the absolute values ​​of the electricity price differences at each time point given in the two calculations is less than 10. -6 .

[0026] Example 2 A simulation example is performed using an improved IEEE 33-node distribution network system. The system diagram is shown below. Figure 2 As shown, node 1 is the connection point between the distribution network and the upstream network. Static var compensators with a maximum capacity of 1Mvar are configured at nodes 6, 16, and 30 respectively. Distributed photovoltaic (PV) systems are connected at nodes 12, 18, 22, and 30 respectively, and electric vehicle charging stations are connected at nodes 18 and 25 respectively. The total maximum output of the distributed PV systems and the base load power of the distribution network are as follows. Figure 3 As shown, Node 18 charging station is located in a residential area, and Node 25 charging station is located in an industrial park. Based on the Monte Carlo method, the grid connection time, grid departure time, initial state of charge, and expected state of charge at departure of 400 electric vehicles are randomly simulated. 200 electric vehicles are allocated to the residential area and 200 to the industrial park. The electric vehicles are considered to be mainstream family models, with rated charging power between 5kW and 10kW and battery capacity between 35kWh and 60kWh. The highest charging price for electric vehicles is 1.0 yuan / kWh, and the lowest charging price is 0.3 yuan / kWh.

[0027] The optimized charging price curve is shown below. Figure 4 As shown, combined with Figure 3As can be seen from the relationship between the maximum total output of photovoltaic power and the base load of the distribution network, between 1:00 and 4:00, the load gradually decreases, and the electricity price gradually decreases to the minimum charging price. This guides electric vehicles at various charging stations to charge during this off-peak period, effectively reducing the peak-valley difference in the power grid. From 4:00 to 9:00, the system load continues to rise, and the charging price rises accordingly, eventually reaching the maximum price of 1.0 yuan / kWh. From 10:00 to 15:00, as the photovoltaic output increases, the photovoltaic output exceeds the electricity load, and the system experiences a problem in absorbing the load. The challenge lies in addressing the issue of peak solar curtailment. To enhance solar grid integration, charging prices are low during these periods, encouraging electric vehicles to charge during these challenging times. Peak curtailment occurs between 11:00 and 12:00, with prices dropping to a minimum of 0.3 yuan / kWh. From 12:00 to 15:00, curtailment gradually decreases, and prices slowly recover. From 16:00 to 21:00, system load climbs to its daily peak, and charging prices remain at their highest level. This avoids concentrated charging of electric vehicles during peak load periods, preventing a "peak-on-peak" situation. After 21:00, system load gradually declines, and charging prices decrease accordingly.

[0028] To illustrate the effectiveness of the method, consider the following strategies for comparison: Strategy 1: Unordered charging of electric vehicles, that is, electric vehicles at each charging station start charging from the moment they are connected to the grid until they reach the desired state of charge or leave the grid, and the distribution network is optimized and scheduled. Strategy 2: Adopt a fixed time-of-use pricing strategy to guide the charging demand of electric vehicles. The time-of-use pricing is shown in Table 1. The optimal charging period is determined with the goal of meeting the continuous charging demand of each electric vehicle during the parking time and minimizing the charging cost, and the distribution network is optimized and scheduled. Strategy 3: Adopt a dynamic electricity price guidance strategy, which simultaneously considers the peak-valley difference and load volatility of the distribution network load in the particle swarm fitness function, optimizes the dynamic electricity price, and determines the optimal charging period with the goal of meeting the continuous charging needs of each electric vehicle during the parking time and minimizing the charging cost, and performs distribution network optimization scheduling. Strategy 4: Adopt a dynamic electricity price guidance strategy, which simultaneously considers the peak-valley difference of the distribution network load, load volatility and photovoltaic curtailment power in the particle swarm fitness function, optimizes the dynamic electricity price, and determines the optimal charging period with the goal of meeting the continuous charging needs of each electric vehicle during the parking time and minimizing the charging cost. The distribution network is then optimized and scheduled, which is the method proposed in this invention.

[0029] Table 1 Time-of-use electricity prices ; The main simulation results under different strategies are shown in Table 2. The load curves of the distribution network before and after the superposition of electric vehicle charging power and the maximum photovoltaic output under strategies 1, 2, 3 and 4 are respectively as follows: Figure 5 , 6 As shown in Figures 7 and 8.

[0030] Table 2 Simulation results for different strategies ; Strategy 1: In the scenario of disordered charging of electric vehicles, the total load curve after charging of electric vehicles is as follows: Figure 5 As shown, since electric vehicles start charging as soon as they are connected to the grid, the charging load overlaps with the peak period of the grid's base load, further increasing the peak load on the basis of the original load and exacerbating the peak-valley difference. The peak-valley difference of Strategy 1 is the largest among all strategies, reaching 4.4659MW. At the same time, the daily operating cost of the distribution network is also the highest among the strategies. It can be seen that Strategy 1 is not conducive to the economical operation of the grid.

[0031] After Strategy 2 adopts time-of-use pricing, the total load curve after electric vehicle charging is as follows: Figure 6 As shown in Table 2, electric vehicles avoid charging during peak load periods due to time-of-use pricing. However, since time-of-use pricing fails to reflect load variation patterns and renewable energy consumption in real time, although electric vehicles charge during off-peak hours to minimize charging costs, the charging time does not accurately correspond to off-peak load periods and peak photovoltaic output periods. Therefore, it fails to achieve the goal of precise load filling during off-peak periods and promote renewable energy consumption. As shown in Table 2, when using time-of-use pricing strategy 2, compared with strategy 1, strategy 2 effectively reduces the peak-valley difference and improves the economic efficiency of distribution network operation, but the photovoltaic consumption rate does not increase.

[0032] When using the dynamic electricity price optimization scheduling of Strategy 3, the total load curve after electric vehicle charging is as follows: Figure 7 As shown in Table 2, charging electric vehicles during periods of lower charging prices and off-peak loads ensures minimal charging costs for users. Building upon Strategy 2, the charging load is precisely matched and superimposed with off-peak load periods, effectively filling the gap in off-peak loads and reducing the peak-valley load difference. During the period from 13:00 to 15:00 when photovoltaic power output is relatively high, the load is in a relatively low period, and the charging price is relatively low. Electric vehicles at each charging station charge during the relatively low load period at noon, which improves the photovoltaic absorption rate compared to Strategies 1 and 2. However, since the midday load trough and the peak photovoltaic output point are out of sync, and the charging price only reflects load fluctuations and fails to reflect photovoltaic output and absorption, it is impossible to accurately guide the charging load to charge during the peak photovoltaic output period. Therefore, the role of electric vehicle charging load in promoting new energy absorption is not fully utilized. As shown in Table 2, Strategy 3 further improves the economic efficiency of grid operation and increases the absorption rate compared to Strategies 1 and 2.

[0033] When using strategy 4 for dynamic electricity price optimization scheduling, the total load curve after electric vehicle charging is as follows: Figure 8 As shown, when Strategy 4 iterates the electricity price, the particle swarm fitness function adds a photovoltaic absorption rate index compared to Strategy 3. Therefore, the charging price reflects both load fluctuations and photovoltaic absorption, accurately guiding electric vehicles to charge during nighttime off-peak hours and midday when photovoltaic power generation is high and absorption is difficult. This effectively reduces the load peak-valley difference and improves the renewable energy absorption rate. According to the data in Table 2, compared to Strategy 3, the load peak-valley difference under Strategy 4 does not change significantly, but the photovoltaic absorption rate is significantly improved. The economic efficiency of the distribution network under Strategy 4 is also the best among all strategies. This shows that while meeting the requirements of reducing the load peak-valley difference and smoothing grid load fluctuations, Strategy 4 further improves the photovoltaic absorption rate and better ensures the economic efficiency of the distribution network operation.

[0034] In summary, simulation results show that the proposed method for coordinated optimization scheduling of electric vehicles and distribution networks, which takes into account the renewable energy absorption rate, can reasonably formulate dynamic charging prices based on the actual grid load and photovoltaic absorption situation. This ensures that the dynamic price value is accurately matched with the grid load distribution and photovoltaic absorption situation, and accurately guides electric vehicles to charge in the corresponding time period, thereby reducing the load peak-valley difference, smoothing load fluctuations, and promoting renewable energy absorption.

Claims

1. A method for coordinated optimization scheduling of electric vehicles and power distribution networks, taking into account the renewable energy absorption rate, characterized in that, Includes the following steps: Step 1: Input the distribution network parameters, basic daily load power, maximum charging price, and minimum charging price. Provide the initial charging price values ​​for each hour of the day, and set the number of iterations. k =1, which is denoted as the first iteration of the particle swarm; Step 2: Transmit the initial charging price value at each hour to each charging station, and each charging station collects the charging information of electric vehicles within the station; Step 3: Based on electric vehicle charging information and charging prices at various times, optimize the scheduling of electric vehicles in each charging station, formulate the optimal charging plan for each electric vehicle, and calculate the total charging power of each charging station at each time. Step 4: Return the total charging power of each charging station at each time to the distribution network. The distribution network will optimize the scheduling of adjustable equipment based on the returned total charging power, distribution network parameters, and basic daily load power, and calculate the optimal power flow of the distribution network after superimposing the charging power of the charging stations. Step 5: Based on the calculated optimal power flow results of the distribution network, calculate the peak-to-valley difference of the total load, load fluctuation, and total power curtailment of distributed generation. Step 6: Incorporate the peak-to-valley difference of the total load of the distribution network, load fluctuation, and the total power curtailment of distributed generation into the fitness function of the particle swarm optimization algorithm to guide the solution of the charging electricity price at each hour of the 24-hour period that meets the constraints. Step 7: Determine if the charging price has reached the convergence condition. If yes, proceed to Step 8; otherwise, let... k = k +1, transmit the obtained charging price to each charging station, and proceed to step three; Step 8: Output the electric vehicle charging price, the total charging power of each charging station at each time, the total load power of the distribution network at each time, and the renewable energy consumption rate.

2. The method for coordinated optimization scheduling of electric vehicles and power distribution networks taking into account the renewable energy absorption rate as described in claim 1, characterized in that, In step one, the basic daily load power of the distribution network is input at the hour, with 24 data points per day. The initial value of the charging price is randomly generated within the range of the highest and lowest charging prices.

3. The method for coordinated optimization scheduling of electric vehicles and power distribution networks taking into account the renewable energy absorption rate as described in claim 1, characterized in that, The electric vehicle charging information in step two includes the grid connection time, disconnection time, initial state of charge, expected state of charge upon disconnection, rated charging power, and battery capacity.

4. The method for coordinated optimization scheduling of electric vehicles and power distribution networks taking into account the renewable energy absorption rate as described in claim 1, characterized in that, In step three, the electric vehicles are optimized and scheduled to meet the continuous charging needs of each electric vehicle within its parking time and minimize charging costs. When an electric vehicle is charging, the charging power is its rated charging power; when not charging, the charging power is zero. The charging power of each electric vehicle at each time point is determined. The total charging power of each charging station at each time point is: ; In the formula, To connect to the node j charging station t Total charging power at any given moment. To connect to the node j The first charging station i electric vehicles t Charging power at any time m This represents the total number of electric vehicles in the charging station.

5. The method for coordinated optimization scheduling of electric vehicles and power distribution networks taking into account the renewable energy absorption rate as described in claim 1, characterized in that: In step four, the distribution network performs optimized scheduling of adjustable equipment and calculates the optimal power flow of the distribution network after superimposing the charging power of the charging stations. The objective function for optimization is: ; In the formula: C operation The total daily operating cost of the power distribution network; C loss and C buy These are the distribution network loss costs and the distribution network's cost of purchasing electricity from the main grid, respectively. C DG This refers to the operating cost of distributed power sources; the specific costs are as follows: ; ; ; In the formula: c loss Cost per unit of network loss; for t Total network loss power at any given time; T The scheduling period is set to 24 hours. for t The price of electricity purchased from the main grid at all times; for t The total active power that is constantly purchased from the main grid; c DG Unit operating cost of distributed power sources; Ω DG The set of nodes connected to distributed generation in a power distribution network; For access nodes i Distributed power sources in t The active power emitted at all times; The constraints for optimization are: 1) Node power balance constraints ; ; In the formula: P jk,t and Q jk,t They are respectively t The line flows through the time jk The active and reactive power; P ij,t and Q ij,t They are respectively t The line flows through the time ij The active and reactive power; l ij，t for t Time flows by ij The square of the branch current; r ij and x ij They represent the lines respectively. ij Resistance and reactance; For access nodes j Distributed power sources in t Efforts made at all times; Indicates that it is connected to the node j charging station t Total charging power at any given moment; and They are respectively t Time Node j The active and reactive power of the base load; Indicates that it is connected to the node j Static var compensator in t Efforts made at all times; 2) Power flow constraints of the line ; ; ; ; In the formula: u i，t for t Time Node i The square of the voltage; and Branch roads ij The lower and upper limits of the active power that can be allowed to flow; and Branch roads ij The lower and upper limits of the reactive power that can flow; branch road ij The maximum current that is allowed to flow; 3) Node voltage constraints ; ; In the formula: u j，t for t Time Node j The square of the voltage; U i,max and U i,min Representing nodes respectively i The upper and lower limits of voltage; 4) Output constraints of distributed power sources ; In the formula: For access nodes i Distributed power sources in t Actual output at any given moment; and Access nodes i Distributed power sources in t The lower and upper limits of output at any given moment; 5) Static Var Compensator Operation Constraints ; In the formula: Indicates that it is connected to the node i Static var compensator in t Efforts made at all times; For access nodes i The capacity of the static var compensator; 6) Power backfeed constraint in distribution network ; In the formula, for t The total active power that the distribution network purchases from the main grid at any given time.

6. The method for coordinated optimization scheduling of electric vehicles and power distribution networks taking into account the renewable energy absorption rate as described in claim 1, characterized in that: The formula for calculating the total load of the distribution network in step five is as follows: ; In the formula, For the distribution network in t Total load power at any given time For the distribution network in t Base load power at any given time To connect to the node j charging station t Total charging power at any given time, Ω EV The set of nodes connected to the electric vehicle charging station; The formula for calculating the peak-valley difference of the total load in a distribution network is: ; In the formula, This represents the maximum value of the total load power of the distribution network. This represents the minimum total load power of the distribution network. The formula for calculating the load fluctuation of the distribution network is as follows: ; In the formula, and For the distribution network at t +1 time and t Total load power at any given time; The formula for calculating the total power curtailment of distributed generation is: ; In the formula, This represents the total power abandoned by distributed power sources. For access nodes i Distributed power sources in t The maximum output at any given moment For access nodes i Distributed power sources in t Actual output at any moment, Ω DG This refers to the set of nodes connected to the distributed power source.

7. The method for coordinated optimization scheduling of electric vehicles and power distribution networks taking into account the renewable energy absorption rate as described in claim 1, characterized in that: The fitness function of the particle swarm optimization algorithm in step six is: ; The constraints for solving the problem using the particle swarm optimization algorithm include: ; In the formula, C max and C min These are the highest and lowest charging prices, respectively. k For the number of particle swarm iterations, n The particle number represents the total number of particles in the population, which is 30. Indicates the first k During the nth iteration n Individual particles t Real-time electricity price information and The first k During the nth iteration n Individual particles t +1 time and t The speed of time, The inertia weighting coefficient is set to a constant of 0.

729. , The individual learning factor and the global learning factor of the particle swarm are taken as constants of 1.49; For the first n The particles from initialization to the... k The electricity price corresponding to the optimal fitness of the generation. For the first k The electricity price with the best fitness across the entire particle history in the next iteration. , The random numbers are uniformly distributed in the range [0,1]. The particle position stores the updated electricity price information for each iteration. The rate at which particles are updated.

8. The method for coordinated optimization scheduling of electric vehicles and power distribution networks taking into account the renewable energy absorption rate as described in claim 1, characterized in that: The convergence criterion in step seven is that the sum of the absolute values ​​of the electricity price differences at each moment given in the two calculations is less than 10. -6 .

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